Fibre tracking magnetic resonance imaging

ABSTRACT

A method of deriving a directional structure from an object dataset is proposed. The object dataset assigns local directions to positions in a multidimensional geometrical space. For example the local directions concern local flow directions in a diffusion tensor magnetic resonance image at least one ‘region of interest’ is selected on the basis of spatial functional information, such as an fMRI image, time correlation of an fMRI image series with a functional paradigm or an anatomical image. These ‘region of interest’ are employed to initialize a fibre tracking to derive the directional structure that represents the nervous fibre system.

The invention pertains to a method of deriving a directional structurefrom an object dataset.

Such a method is known from the international application WO 01/91639.

The known method concerns a reconstruction of an image of brain fibersfrom diffusion weighted magnetic resonance images with various diffusionsensitizing gradient combination and strengths. This representation ofthe brain fiber structure forms the directional structure. The imageinformation of the diffusion weighted magnetic resonance images is madeavailable in the form of pixels. The intensity of individual pixels arefitted to calculate six independent variables in a 3×3 diffusion tensor.The diffusion tensor is then diagonalized to obtain three eigenvaluesand the corresponding three eigenvectors. These eigenvectors representthe local direction of the brain fibre structure at the pixel at issue.Accordingly, the diffusion weighted magnetic resonance images assign thelocal direction of the brain fibers to positions in the brain.

The known method employs fibre tracking to establish the brain fibrestructure. The fibre tracking employed in the known method consists ofthree parts: initiation of brain fibre tracking, pixel connecting andjudgement of the termination of the fibers. Tracking of projections offibers is initiated in a three-dimensional space arbitrarily chosen bythe user and propagated in both directions according to the direction ofthe fibre (the eigenvector associated with the largest eigenvalues. Eachtime the tracking laves a pixel to the next pixel, judgement is madewhether the fibre is continuous or terminated based on randomness of thefibre orientation of the adjacent pixels.

An object of the invention is to provide an method of deriving adirectional structure which requires less computational effort than theknown method.

This object is achieved by the method of deriving a directionalstructure according to the invention which comprises the steps of

-   -   selecting at least one region of interest in the        multi-dimensional geometrical space on the basis of spatial        functional information    -   selecting candidate positions in the selected region of interest    -   establish the directional structure on the basis of        -   dominant local directions at the candidate positions and        -   relative locations of these candidate positions in the            multi-dimensional geometrical space.

The object dataset represents directional information concerning anobject being examined. In particular the object is a patient to beexamined and the examination relates to the patient's brain and nervoussystem. For example the directional information concerns local directionof anisotropic water diffusion in the brain and nervous system. From theobject dataset the directional structure is established on the basis ofdominant local directions. When applied to the patients brain andnervous system, this directional structure represents the way the axonaltracks are organized and allows study of the spatial architecture ofwhite matter tracts. Establishing of the directional structure can beachieved by so-called fibre tracking algorithms. These fibre trackingalgorithms are known as such from the review article ‘Fiber tracking:principles and strategies—a technical review’ in NMR Biomed.15(2002)468-480 by S. Mori and Peter C. M. van Zijl. Fibre trackinginvolves reconstruction of the directional structure by following thelocal dominant direction from several candidate positions or seedpoints. In order to avoid an exhaustive search involving all positionsin the geometrical space as candidate positions, the selection ofcandidate positions is limited to one or a few ‘regions of interest’.The invention achieves a further improvement in the efficiently ofselecting candidate positions in that with a low number of candidatepositions, a quite complete reconstruction of the directional structureof interest. To this end, the ‘region of interest’ is selected on thebasis of spatial functional information that relates to the same objectand the object dataset itself.

These and other aspects of the invention will be further elaborated withreference to the embodiments defined in the dependent Claims.

Preferably, said spatial functional information is represented byfunctional magnetic resonance image(s) that represent the functioning ofthe brain and nervous system. For example, the ‘regions of interest’ arechosen as regions where brain activity at issue shows up in thefunctional image.

Further improvement of the efficiency of the selection of the candidatepositions is achieved in that the ‘regions of interest’ are selected onthe basis of a correlation of a paradigm and the functional magneticresonance image that is acquired during performance of the paradigm.Such a paradigm concerns a task set to the patient to be examined, suchas finger tapping or viewing a pattern.

In a further preferred implementation the correlation of the functionalmagnetic resonance image with the paradigm is compared to a pre-setthreshold for several positions in the geometrical space. The ‘regionsof interest’ are then selected on the basis of areas in the geometricalspace where the correlation exceeds the pre-set threshold. In this wayit appears that the ‘regions of interest’ can be selected automatically.These thus selected ‘regions of interest’ are very efficient in that thedirectional structure is accurately reconstructed on the basis of asmall number of candidate positions.

The invention further relates to a workstation as defined in Claim 8.The workstation of the invention is arranged to receive the objectdataset, such as in the form of DTI-images and is further arranged toperform the method of the invention. The invention also relates to acomputer program as defined in Claim 9. The computer program of theinvention can be provided on a datacarrier such as a CD-rom, but mayalso be downloaded from a data-network such as the world-wide web. Whenthe computer program is downloaded to the working memory of aworkstation, the instructions in the computer program cause theworkstation to perform the method of the invention.

These and other aspects of the invention will be elucidated withreference to the embodiments described hereinafter and with reference tothe accompanying drawing wherein

FIG. 1 shows a diagrammatic representation of the method according tothe invention.

In the diagrammatic representation of the method as shown in FIG. 1, theobject dataset is formed by a diffusion tensor magnetic resonance imagedataset 1 (DTI-dataset). The DTI-image is generated from an magneticresonance imaging system 6. The DTI-dataset 1 represents locally mainflow directions of anisotropic flow of in white brain tissue. TheDTI-dataset 1 may be a three-dimensional image which assigns in itsvoxels the local main flow direction to a position in three-dimensionalvolumetric space. The DTI-dataset 1 can be applied to a monitor in orderto display a DTI-image. To the DTI-dataset a fibre-tracking algorithm isapplied to derive from the DTI-image the directional structure 2 in theform of a fibre structure which is a representation of the nervous fibresystem of the patient to be examined. In order to avoid an exhaustivesearch involving all positions in the geometrical space as candidatepositions, the selection of candidate positions is limited to one or afew ‘regions of interest’ 3. Initial candidate positions to search forfibers in the DTI-dataset are selected in the ‘regions of interest’ Asthe fibre structure is being formed, additional candidate points arechosen near the current fibre structure as it is being built up. The‘regions of interest’ 3 are selected on the basis of functionalinformation. Such functional information is for example obtained from afunctional magnetic resonance image (fMRI-image) 4. The fMRI-image isfor example generated during the same examination of the patient to beexamined in the magnetic resonance imaging system 6. This fMRI-imageshows local regions of activity in for example the patient's brain.According to the invention, the ‘regions of interest’ are selected asregions in which there is brain activity visible in the fMRI-image.Accordingly, the fibre system connecting these region of brain activityare easily derived from the DTI-image 1. Notably, spurious efforts infibre tracking any fibers that are irrelevant for the connection of thebrain regions at issue are avoided. The ‘regions of interest’ 3 may bederived also from an anatomical image 5, as an alternative to ortogether with the use of the fMRI to support the selection of the‘regions of interest’. The anatomical image 5 may be generated by themagnetic resonance imaging system 6, but may also be generated byanother imaging modality, such as a computed-tomography system 7. Fromthe anatomical image 5, the relevant regions of the anatomy, such as thebrain, which relate to particular nervous functions are shown. Alsotumors be visible from the anatomical image and or form the fMRI imageand the ‘region of interest’ are then easily selected to correspond tothese tumors and the Thus, also the anatomical image includes functionalinformation that is useful to support the selection of the ‘regions ofinterest’ 3.

Often, a time series of fMRI-images is formed. The image information inthe successive fMRI-images, e.g. as represented by the brightnessvalues, is preferably correlated with a paradigm that is exercised bythe patient to be examined. Such a paradigm nay involve a task, such asfinger tapping or viewing a simple optical pattern that has a simplevariation in time. In a correlation step 9, a time-correlation of theparadigm 8 with the fMRI-images 4 is computed. Subsequently, in acomparison step 10, the time-correlation is compared to a thresholdvalue 11. This threshold value 11 may be input or adjusted by the user,or may have been stored in advance. Portions of the fMRI-images in whichthe time-correlation with the paradigm exceeds the threshold areautomatically designated as ‘regions of interest’. Notably theseportions of the fMRI-image relates to areas where there is substantialnervous activity. The functions of the method of the invention are inpractice performed by means of a workstation in which a computer programwith instructions to perform these functions is loaded.

1. A method of deriving a directional structure from a diffusion tensormagnetic resonance imaging object dataset, the method comprising thesteps of: selecting at least one region of interest in themulti-dimensional geometrical space on the basis of spatial functionalinformation, the spatial functional information including informationderived from a functional magnetic resonance image data; selectingcandidate positions in the selected region of interest; establishing thedirectional structure on the basis of: dominant local directions at thecandidate positions, and relative locations of these candidate positionsin the multi-dimensional geometrical space, wherein the at least oneregion of interest is determined on the basis of the functional magneticresonance image; and generating an image based on the directionalstructure.
 2. A method of deriving a directional structure as claimed inclaim 1, wherein, two or more regions of interest are selected and thecandidate positions are selected in said regions of interest, and thedirectional structure is established in as far as passing through theselected regions of interest.
 3. A method of deriving a directionalstructure as claimed in claim 1, wherein further candidate positions areselected on the basis of a currently established directional structure.4. A method of deriving a directional structure as claimed in claim 1,wherein further candidate positions are selected in a neighborhood of atleast one of the regions of interest.
 5. A method of deriving adirectional structure from a diffusion tensor magnetic resonance imagingobject dataset, the method comprising: selecting at least one region ofinterest in the multi-dimensional geometrical space on the basis ofspatial functional information the spatial functional informationincluding information derived from a functional magnetic resonance imagedata, the selecting including forming a correlation of the functionalmagnetic resonance image with a pre-determined paradigm and determiningthe at least one region of interest on the basis of said correlation;selecting candidate positions in the selected region of interest;establishing the directional structure on the basis of: dominant localdirections at the candidate positions, and relative locations of thesecandidate positions in the multi-dimensional geometrical space, whereinthe at least one region of interest is determined on the basis of thefunctional magnetic resonance image; and generating an image based onthe directional structure.
 6. A method of deriving a directionalstructure as claimed in claim 5, wherein, the correlation of thefunctional magnetic resonance image with a pre-determined paradigm iscompared to a pre-set threshold, and at least one region in thefunctional magnetic resonance image is selected where the correlationexceeds the pre-set threshold; the at least one region of interest isdetermined on the basis of said selected region in the functionalmagnetic resonance image.